Artificial Intelligence Nanodegree

Computer Vision Capstone

Project: Facial Keypoint Detection


Welcome to the final Computer Vision project in the Artificial Intelligence Nanodegree program!

In this project, you’ll combine your knowledge of computer vision techniques and deep learning to build and end-to-end facial keypoint recognition system! Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition.

There are three main parts to this project:

Part 1 : Investigating OpenCV, pre-processing, and face detection

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!


*Here's what you need to know to complete the project:

  1. In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested.

    a. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

  1. In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation.

    a. Each section where you will answer a question is preceded by a 'Question X' header.

    b. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional suggestions for enhancing the project beyond the minimum requirements. If you decide to pursue the "(Optional)" sections, you should include the code in this IPython notebook.

Your project submission will be evaluated based on your answers to each of the questions and the code implementations you provide.

Steps to Complete the Project

Each part of the notebook is further broken down into separate steps. Feel free to use the links below to navigate the notebook.

In this project you will get to explore a few of the many computer vision algorithms built into the OpenCV library. This expansive computer vision library is now almost 20 years old and still growing!

The project itself is broken down into three large parts, then even further into separate steps. Make sure to read through each step, and complete any sections that begin with '(IMPLEMENTATION)' in the header; these implementation sections may contain multiple TODOs that will be marked in code. For convenience, we provide links to each of these steps below.

Part 1 : Investigating OpenCV, pre-processing, and face detection

  • Step 0: Detect Faces Using a Haar Cascade Classifier
  • Step 1: Add Eye Detection
  • Step 2: De-noise an Image for Better Face Detection
  • Step 3: Blur an Image and Perform Edge Detection
  • Step 4: Automatically Hide the Identity of an Individual

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

  • Step 5: Create a CNN to Recognize Facial Keypoints
  • Step 6: Compile and Train the Model
  • Step 7: Visualize the Loss and Answer Questions

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!

  • Step 8: Build a Robust Facial Keypoints Detector (Complete the CV Pipeline)

Step 0: Detect Faces Using a Haar Cascade Classifier

Have you ever wondered how Facebook automatically tags images with your friends' faces? Or how high-end cameras automatically find and focus on a certain person's face? Applications like these depend heavily on the machine learning task known as face detection - which is the task of automatically finding faces in images containing people.

At its root face detection is a classification problem - that is a problem of distinguishing between distinct classes of things. With face detection these distinct classes are 1) images of human faces and 2) everything else.

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the detector_architectures directory.

Import Resources

In the next python cell, we load in the required libraries for this section of the project.

In [1]:
# Import required libraries for this section

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
import math
import cv2                     # OpenCV library for computer vision
from PIL import Image
import time 

Next, we load in and display a test image for performing face detection.

Note: by default OpenCV assumes the ordering of our image's color channels are Blue, then Green, then Red. This is slightly out of order with most image types we'll use in these experiments, whose color channels are ordered Red, then Green, then Blue. In order to switch the Blue and Red channels of our test image around we will use OpenCV's cvtColor function, which you can read more about by checking out some of its documentation located here. This is a general utility function that can do other transformations too like converting a color image to grayscale, and transforming a standard color image to HSV color space.

In [2]:
# Load in color image for face detection
image = cv2.imread('images/test_image_1.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot our image using subplots to specify a size and title
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[2]:
<matplotlib.image.AxesImage at 0x7ff38ec0e0b8>

There are a lot of people - and faces - in this picture. 13 faces to be exact! In the next code cell, we demonstrate how to use a Haar Cascade classifier to detect all the faces in this test image.

This face detector uses information about patterns of intensity in an image to reliably detect faces under varying light conditions. So, to use this face detector, we'll first convert the image from color to grayscale.

Then, we load in the fully trained architecture of the face detector -- found in the file haarcascade_frontalface_default.xml - and use it on our image to find faces!

To learn more about the parameters of the detector see this post.

In [3]:
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[3]:
<matplotlib.image.AxesImage at 0x7ff38e317a58>

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.


Step 1: Add Eye Detections

There are other pre-trained detectors available that use a Haar Cascade Classifier - including full human body detectors, license plate detectors, and more. A full list of the pre-trained architectures can be found here.

To test your eye detector, we'll first read in a new test image with just a single face.

In [4]:
# Load in color image for face detection
image = cv2.imread('images/james.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot the RGB image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[4]:
<matplotlib.image.AxesImage at 0x7ff38e2f4cf8>

Notice that even though the image is a black and white image, we have read it in as a color image and so it will still need to be converted to grayscale in order to perform the most accurate face detection.

So, the next steps will be to convert this image to grayscale, then load OpenCV's face detector and run it with parameters that detect this face accurately.

In [5]:
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 1.25, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detection')
ax1.imshow(image_with_detections)
Number of faces detected: 1
Out[5]:
<matplotlib.image.AxesImage at 0x7ff38e2ce6d8>

(IMPLEMENTATION) Add an eye detector to the current face detection setup.

A Haar-cascade eye detector can be included in the same way that the face detector was and, in this first task, it will be your job to do just this.

To set up an eye detector, use the stored parameters of the eye cascade detector, called haarcascade_eye.xml, located in the detector_architectures subdirectory. In the next code cell, create your eye detector and store its detections.

A few notes before you get started:

First, make sure to give your loaded eye detector the variable name

eye_cascade

and give the list of eye regions you detect the variable name

eyes

Second, since we've already run the face detector over this image, you should only search for eyes within the rectangular face regions detected in faces. This will minimize false detections.

Lastly, once you've run your eye detector over the facial detection region, you should display the RGB image with both the face detection boxes (in red) and your eye detections (in green) to verify that everything works as expected.

In [6]:
# Make a copy of the original image to plot rectangle detections
image_with_detections = np.copy(image)   

# Loop over the detections and draw their corresponding face detection boxes
for (x,y,w,h) in faces:
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)  
    
# Do not change the code above this comment!

    
## TODO: Add eye detection, using haarcascade_eye.xml, to the current face detector algorithm
## TODO: Loop over the eye detections and draw their corresponding boxes in green on image_with_detections

#Extract the pre-trained eye detector from an xml file
eye_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_eye.xml')

# Loop over the face detections and search area for eyes
for (x,y,w,h) in faces:
    gray_face = gray[y:y+h, x:x+w]
    image_face = image_with_detections[y:y+h, x:x+w]
    
    # Detect the eyes in face detection region
    eyes = eye_cascade.detectMultiScale(gray_face, 1.2, 3)
    
    # Print the number of eyes detected in the image
    print('Number of eyes detected:', len(eyes))
    
    # Loop over eye detections and draw detection boxes
    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(image_face, (ex,ey), (ex+ew,ey+eh),(0,255,0), 3) 

# Plot the image with both faces and eyes detected
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face and Eye Detection')
ax1.imshow(image_with_detections)
Number of eyes detected: 2
Out[6]:
<matplotlib.image.AxesImage at 0x7ff38e231860>

(Optional) Add face and eye detection to your laptop camera

It's time to kick it up a notch, and add face and eye detection to your laptop's camera! Afterwards, you'll be able to show off your creation like in the gif shown below - made with a completed version of the code!

Notice that not all of the detections here are perfect - and your result need not be perfect either. You should spend a small amount of time tuning the parameters of your detectors to get reasonable results, but don't hold out for perfection. If we wanted perfection we'd need to spend a ton of time tuning the parameters of each detector, cleaning up the input image frames, etc. You can think of this as more of a rapid prototype.

The next cell contains code for a wrapper function called laptop_camera_face_eye_detector that, when called, will activate your laptop's camera. You will place the relevant face and eye detection code in this wrapper function to implement face/eye detection and mark those detections on each image frame that your camera captures.

Before adding anything to the function, you can run it to get an idea of how it works - a small window should pop up showing you the live feed from your camera; you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [ ]:
### Add face and eye detection to this laptop camera function 
# Make sure to draw out all faces/eyes found in each frame on the shown video feed

import cv2
import time 

# wrapper function for face/eye detection with your laptop camera
def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep the video stream open
    while rval:
        # Plot the image from camera with all the face and eye detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # Exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()
            
            # Make sure window closes on OSx
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
In [ ]:
# Call the laptop camera face/eye detector function above
laptop_camera_go()

Step 2: De-noise an Image for Better Face Detection

Image quality is an important aspect of any computer vision task. Typically, when creating a set of images to train a deep learning network, significant care is taken to ensure that training images are free of visual noise or artifacts that hinder object detection. While computer vision algorithms - like a face detector - are typically trained on 'nice' data such as this, new test data doesn't always look so nice!

When applying a trained computer vision algorithm to a new piece of test data one often cleans it up first before feeding it in. This sort of cleaning - referred to as pre-processing - can include a number of cleaning phases like blurring, de-noising, color transformations, etc., and many of these tasks can be accomplished using OpenCV.

In this short subsection we explore OpenCV's noise-removal functionality to see how we can clean up a noisy image, which we then feed into our trained face detector.

Create a noisy image to work with

In the next cell, we create an artificial noisy version of the previous multi-face image. This is a little exaggerated - we don't typically get images that are this noisy - but image noise, or 'grainy-ness' in a digitial image - is a fairly common phenomenon.

In [7]:
# Load in the multi-face test image again
image = cv2.imread('images/test_image_1.jpg')

# Convert the image copy to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Make an array copy of this image
image_with_noise = np.asarray(image)

# Create noise - here we add noise sampled randomly from a Gaussian distribution: a common model for noise
noise_level = 40
noise = np.random.randn(image.shape[0],image.shape[1],image.shape[2])*noise_level

# Add this noise to the array image copy
image_with_noise = image_with_noise + noise

# Convert back to uint8 format
image_with_noise = np.asarray([np.uint8(np.clip(i,0,255)) for i in image_with_noise])

# Plot our noisy image!
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image')
ax1.imshow(image_with_noise)
Out[7]:
<matplotlib.image.AxesImage at 0x7ff38e20f588>

In the context of face detection, the problem with an image like this is that - due to noise - we may miss some faces or get false detections.

In the next cell we apply the same trained OpenCV detector with the same settings as before, to see what sort of detections we get.

In [8]:
# Convert the RGB  image to grayscale
gray_noise = cv2.cvtColor(image_with_noise, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_noise, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image_with_noise)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 11
Out[8]:
<matplotlib.image.AxesImage at 0x7ff38e160da0>

With this added noise we now miss one of the faces!

(IMPLEMENTATION) De-noise this image for better face detection

Time to get your hands dirty: using OpenCV's built in color image de-noising functionality called fastNlMeansDenoisingColored - de-noise this image enough so that all the faces in the image are properly detected. Once you have cleaned the image in the next cell, use the cell that follows to run our trained face detector over the cleaned image to check out its detections.

You can find its official documentation here and a useful example here.

Note: you can keep all parameters except photo_render fixed as shown in the second link above. Play around with the value of this parameter - see how it affects the resulting cleaned image.

In [9]:
## TODO: Use OpenCV's built in color image de-noising function to clean up our noisy image!

denoised_image = cv2.fastNlMeansDenoisingColored(image_with_noise,None,10,10,7,21) # your final de-noised image (should be RGB)

# Plot denoised image
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Denoised Image')
ax1.imshow(denoised_image)
Out[9]:
<matplotlib.image.AxesImage at 0x7ff38e140438>
In [10]:
## TODO: Run the face detector on the de-noised image to improve your detections and display the result

# Convert the RGB  image to grayscale
gray_denoise = cv2.cvtColor(denoised_image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_denoise, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(denoised_image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Denoised Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 12
Out[10]:
<matplotlib.image.AxesImage at 0x7ff3845edc88>

Step 3: Blur an Image and Perform Edge Detection

Now that we have developed a simple pipeline for detecting faces using OpenCV - let's start playing around with a few fun things we can do with all those detected faces!

Importance of Blur in Edge Detection

Edge detection is a concept that pops up almost everywhere in computer vision applications, as edge-based features (as well as features built on top of edges) are often some of the best features for e.g., object detection and recognition problems.

Edge detection is a dimension reduction technique - by keeping only the edges of an image we get to throw away a lot of non-discriminating information. And typically the most useful kind of edge-detection is one that preserves only the important, global structures (ignoring local structures that aren't very discriminative). So removing local structures / retaining global structures is a crucial pre-processing step to performing edge detection in an image, and blurring can do just that.

Below is an animated gif showing the result of an edge-detected cat taken from Wikipedia, where the image is gradually blurred more and more prior to edge detection. When the animation begins you can't quite make out what it's a picture of, but as the animation evolves and local structures are removed via blurring the cat becomes visible in the edge-detected image.

Edge detection is a convolution performed on the image itself, and you can read about Canny edge detection on this OpenCV documentation page.

Canny edge detection

In the cell below we load in a test image, then apply Canny edge detection on it. The original image is shown on the left panel of the figure, while the edge-detected version of the image is shown on the right. Notice how the result looks very busy - there are too many little details preserved in the image before it is sent to the edge detector. When applied in computer vision applications, edge detection should preserve global structure; doing away with local structures that don't help describe what objects are in the image.

In [11]:
# Load in the image
image = cv2.imread('images/fawzia.jpg')

# Convert to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  

# Perform Canny edge detection
edges = cv2.Canny(gray,100,200)

# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)

# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)

ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])

ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[11]:
<matplotlib.image.AxesImage at 0x7ff384569940>

Without first blurring the image, and removing small, local structures, a lot of irrelevant edge content gets picked up and amplified by the detector (as shown in the right panel above).

(IMPLEMENTATION) Blur the image then perform edge detection

In the next cell, you will repeat this experiment - blurring the image first to remove these local structures, so that only the important boudnary details remain in the edge-detected image.

Blur the image by using OpenCV's filter2d functionality - which is discussed in this documentation page - and use an averaging kernel of width equal to 4.

In [12]:
### TODO: Blur the test imageusing OpenCV's filter2d functionality, 
# Use an averaging kernel, and a kernel width equal to 4

## TODO: Then perform Canny edge detection and display the output

# Load in the image
image = cv2.imread('images/fawzia.jpg')

# Convert to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  

# Blur image
kernel = np.ones((4,4),np.float32)/16
blurred_gray = cv2.filter2D(gray,-1,kernel)

# Perform Canny edge detection
edges = cv2.Canny(blurred_gray,100,200)

# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)

# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Blurred Image')
ax1.imshow(blurred_gray)

ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])

ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[12]:
<matplotlib.image.AxesImage at 0x7ff384515be0>

Step 4: Automatically Hide the Identity of an Individual

If you film something like a documentary or reality TV, you must get permission from every individual shown on film before you can show their face, otherwise you need to blur it out - by blurring the face a lot (so much so that even the global structures are obscured)! This is also true for projects like Google's StreetView maps - an enormous collection of mapping images taken from a fleet of Google vehicles. Because it would be impossible for Google to get the permission of every single person accidentally captured in one of these images they blur out everyone's faces, the detected images must automatically blur the identity of detected people. Here's a few examples of folks caught in the camera of a Google street view vehicle.

Read in an image to perform identity detection

Let's try this out for ourselves. Use the face detection pipeline built above and what you know about using the filter2D to blur and image, and use these in tandem to hide the identity of the person in the following image - loaded in and printed in the next cell.

In [13]:
# Load in the image
image = cv2.imread('images/gus.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Display the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[13]:
<matplotlib.image.AxesImage at 0x7ff384431518>

(IMPLEMENTATION) Use blurring to hide the identity of an individual in an image

The idea here is to 1) automatically detect the face in this image, and then 2) blur it out! Make sure to adjust the parameters of the averaging blur filter to completely obscure this person's identity.

In [14]:
## TODO: Implement face detection

## TODO: Blur the bounding box around each detected face using an averaging filter and display the result

# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 1.4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_faceblur = np.copy(image)

# Get the bounding box for each detected face and blur area
for (x,y,w,h) in faces:
    image_face = image_with_faceblur[y:y+h, x:x+w]
    # Blur face in image
    image_with_faceblur[y:y+h, x:x+w] = cv2.blur(image_face,(100,100))

# Display the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Blurred Image')
ax1.imshow(image_with_faceblur)
Number of faces detected: 1
Out[14]:
<matplotlib.image.AxesImage at 0x7ff38440e240>

(Optional) Build identity protection into your laptop camera

In this optional task you can add identity protection to your laptop camera, using the previously completed code where you added face detection to your laptop camera - and the task above. You should be able to get reasonable results with little parameter tuning - like the one shown in the gif below.

As with the previous video task, to make this perfect would require significant effort - so don't strive for perfection here, strive for reasonable quality.

The next cell contains code a wrapper function called laptop_camera_identity_hider that - when called - will activate your laptop's camera. You need to place the relevant face detection and blurring code developed above in this function in order to blur faces entering your laptop camera's field of view.

Before adding anything to the function you can call it to get a hang of how it works - a small window will pop up showing you the live feed from your camera, you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [ ]:
### Insert face detection and blurring code into the wrapper below to create an identity protector on your laptop!
import cv2
import time 

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        # Plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # Exit by pressing any key
            # Destroy windows
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [ ]:
# Run laptop identity hider
laptop_camera_go()

Step 5: Create a CNN to Recognize Facial Keypoints

OpenCV is often used in practice with other machine learning and deep learning libraries to produce interesting results. In this stage of the project you will create your own end-to-end pipeline - employing convolutional networks in keras along with OpenCV - to apply a "selfie" filter to streaming video and images.

You will start by creating and then training a convolutional network that can detect facial keypoints in a small dataset of cropped images of human faces. We then guide you towards OpenCV to expanding your detection algorithm to more general images. What are facial keypoints? Let's take a look at some examples.

Facial keypoints (also called facial landmarks) are the small blue-green dots shown on each of the faces in the image above - there are 15 keypoints marked in each image. They mark important areas of the face - the eyes, corners of the mouth, the nose, etc. Facial keypoints can be used in a variety of machine learning applications from face and emotion recognition to commercial applications like the image filters popularized by Snapchat.

Below we illustrate a filter that, using the results of this section, automatically places sunglasses on people in images (using the facial keypoints to place the glasses correctly on each face). Here, the facial keypoints have been colored lime green for visualization purposes.

Make a facial keypoint detector

But first things first: how can we make a facial keypoint detector? Well, at a high level, notice that facial keypoint detection is a regression problem. A single face corresponds to a set of 15 facial keypoints (a set of 15 corresponding $(x, y)$ coordinates, i.e., an output point). Because our input data are images, we can employ a convolutional neural network to recognize patterns in our images and learn how to identify these keypoint given sets of labeled data.

In order to train a regressor, we need a training set - a set of facial image / facial keypoint pairs to train on. For this we will be using this dataset from Kaggle. We've already downloaded this data and placed it in the data directory. Make sure that you have both the training and test data files. The training dataset contains several thousand $96 \times 96$ grayscale images of cropped human faces, along with each face's 15 corresponding facial keypoints (also called landmarks) that have been placed by hand, and recorded in $(x, y)$ coordinates. This wonderful resource also has a substantial testing set, which we will use in tinkering with our convolutional network.

To load in this data, run the Python cell below - notice we will load in both the training and testing sets.

The load_data function is in the included utils.py file.

In [15]:
from utils import *

# Load training set
X_train, y_train = load_data()
print("X_train.shape == {}".format(X_train.shape))
print("y_train.shape == {}; y_train.min == {:.3f}; y_train.max == {:.3f}".format(
    y_train.shape, y_train.min(), y_train.max()))

# Load testing set
X_test, _ = load_data(test=True)
print("X_test.shape == {}".format(X_test.shape))
Using TensorFlow backend.
X_train.shape == (2140, 96, 96, 1)
y_train.shape == (2140, 30); y_train.min == -0.920; y_train.max == 0.996
X_test.shape == (1783, 96, 96, 1)

The load_data function in utils.py originates from this excellent blog post, which you are strongly encouraged to read. Please take the time now to review this function. Note how the output values - that is, the coordinates of each set of facial landmarks - have been normalized to take on values in the range $[-1, 1]$, while the pixel values of each input point (a facial image) have been normalized to the range $[0,1]$.

Note: the original Kaggle dataset contains some images with several missing keypoints. For simplicity, the load_data function removes those images with missing labels from the dataset. As an optional extension, you are welcome to amend the load_data function to include the incomplete data points.

Visualize the Training Data

Execute the code cell below to visualize a subset of the training data.

In [16]:
import matplotlib.pyplot as plt
%matplotlib inline

fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X_train[i], y_train[i], ax)

For each training image, there are two landmarks per eyebrow (four total), three per eye (six total), four for the mouth, and one for the tip of the nose.

Review the plot_data function in utils.py to understand how the 30-dimensional training labels in y_train are mapped to facial locations, as this function will prove useful for your pipeline.

(IMPLEMENTATION) Specify the CNN Architecture

In this section, you will specify a neural network for predicting the locations of facial keypoints. Use the code cell below to specify the architecture of your neural network. We have imported some layers that you may find useful for this task, but if you need to use more Keras layers, feel free to import them in the cell.

Your network should accept a $96 \times 96$ grayscale image as input, and it should output a vector with 30 entries, corresponding to the predicted (horizontal and vertical) locations of 15 facial keypoints. If you are not sure where to start, you can find some useful starting architectures in this blog, but you are not permitted to copy any of the architectures that you find online.

In [17]:
# Import deep learning resources from Keras
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Dropout, Activation
from keras.layers import Flatten, Dense

# fix random seed for reproducibility
seed = 7
np.random.seed(seed)

## TODO: Specify a CNN architecture
# Your model should accept 96x96 pixel graysale images in
# It should have a fully-connected output layer with 30 values (2 for each facial keypoint)
def create_model(optimizer='RMSprop'):
    model = Sequential()

    model.add(Convolution2D(filters=16, kernel_size=2, padding='same',
                 input_shape=(96,96,1))) #X_train.shape[1:]
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))

    model.add(Convolution2D(filters=32, kernel_size=2, padding='same'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))

    model.add(Convolution2D(filters=64, kernel_size=2, padding='same'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))

    model.add(Convolution2D(filters=128, kernel_size=2, padding='same'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))

    model.add(Flatten())

    model.add(Dense(300))
    model.add(Activation('relu'))
    model.add(Dropout(0.50))


    model.add(Dense(30, activation='linear'))

    # Summarize the model
    model.summary()
    
    # Compile
    model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['accuracy'])
    return model

model = create_model('RMSprop')
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 96, 96, 16)        80        
_________________________________________________________________
activation_1 (Activation)    (None, 96, 96, 16)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 48, 48, 16)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 48, 48, 16)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 48, 48, 32)        2080      
_________________________________________________________________
activation_2 (Activation)    (None, 48, 48, 32)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 24, 24, 32)        0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 24, 24, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 24, 24, 64)        8256      
_________________________________________________________________
activation_3 (Activation)    (None, 24, 24, 64)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64)        0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 12, 12, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 12, 12, 128)       32896     
_________________________________________________________________
activation_4 (Activation)    (None, 12, 12, 128)       0         
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 6, 6, 128)         0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 6, 6, 128)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 4608)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 300)               1382700   
_________________________________________________________________
activation_5 (Activation)    (None, 300)               0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 300)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 30)                9030      
=================================================================
Total params: 1,435,042
Trainable params: 1,435,042
Non-trainable params: 0
_________________________________________________________________

Brainstorming: What can change in the model?

  • Number of Convolution layers
  • Kernal size of Conv layer
  • Stride of Conv Layer
  • Flatten vs GlobalAveragePooling2D
  • Size and Number of dense layers
  • Activations of the layers
  • Dropout values
  • Batch size

Step 6: Compile and Train the Model

After specifying your architecture, you'll need to compile and train the model to detect facial keypoints'

(IMPLEMENTATION) Compile and Train the Model

Use the compile method to configure the learning process. Experiment with your choice of optimizer; you may have some ideas about which will work best (SGD vs. RMSprop, etc), but take the time to empirically verify your theories.

Use the fit method to train the model. Break off a validation set by setting validation_split=0.2. Save the returned History object in the history variable.

Your model is required to attain a validation loss (measured as mean squared error) of at least XYZ. When you have finished training, save your model as an HDF5 file with file path my_model.h5.

In [18]:
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam
from keras.callbacks import ModelCheckpoint  

## TODO: Compile the model
model.compile(optimizer='RMSprop', loss='mean_squared_error', metrics=['accuracy'])

# Checkpoint callback
checkpointer = ModelCheckpoint(filepath='my_model.h5', 
                               verbose=1, save_best_only=True)

## TODO: Train the model
hist = model.fit(x=X_train, y=y_train, 
          validation_split=0.2,
          epochs=400, batch_size=200, callbacks=[checkpointer], verbose=1)

## TODO: Save the model as model.h5
# Handled by checkpoint callback
Train on 1712 samples, validate on 428 samples
Epoch 1/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.1845 - acc: 0.2037Epoch 00000: val_loss improved from inf to 0.08380, saving model to my_model.h5
1712/1712 [==============================] - 3s - loss: 0.1751 - acc: 0.2085 - val_loss: 0.0838 - val_acc: 0.6963
Epoch 2/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0381 - acc: 0.3150Epoch 00001: val_loss improved from 0.08380 to 0.06889, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0371 - acc: 0.3172 - val_loss: 0.0689 - val_acc: 0.6963
Epoch 3/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0311 - acc: 0.3271Epoch 00002: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0315 - acc: 0.3417 - val_loss: 0.0785 - val_acc: 0.6963
Epoch 4/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0314 - acc: 0.3350Epoch 00003: val_loss improved from 0.06889 to 0.04439, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0307 - acc: 0.3446 - val_loss: 0.0444 - val_acc: 0.6963
Epoch 5/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0266 - acc: 0.4036Epoch 00004: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0269 - acc: 0.4048 - val_loss: 0.0620 - val_acc: 0.6963
Epoch 6/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0247 - acc: 0.4014Epoch 00005: val_loss improved from 0.04439 to 0.02870, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0245 - acc: 0.4106 - val_loss: 0.0287 - val_acc: 0.6963
Epoch 7/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0197 - acc: 0.4350Epoch 00006: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0195 - acc: 0.4363 - val_loss: 0.0417 - val_acc: 0.6963
Epoch 8/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0194 - acc: 0.4243Epoch 00007: val_loss improved from 0.02870 to 0.01946, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0188 - acc: 0.4317 - val_loss: 0.0195 - val_acc: 0.6963
Epoch 9/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0161 - acc: 0.4671Epoch 00008: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0163 - acc: 0.4609 - val_loss: 0.0349 - val_acc: 0.6963
Epoch 10/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0152 - acc: 0.5119Epoch 00009: val_loss improved from 0.01946 to 0.01227, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0153 - acc: 0.5134 - val_loss: 0.0123 - val_acc: 0.6963
Epoch 11/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0131 - acc: 0.5000Epoch 00010: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0131 - acc: 0.4982 - val_loss: 0.0241 - val_acc: 0.6963
Epoch 12/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0131 - acc: 0.5143Epoch 00011: val_loss improved from 0.01227 to 0.00947, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0131 - acc: 0.5140 - val_loss: 0.0095 - val_acc: 0.6963
Epoch 13/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0114 - acc: 0.5379Epoch 00012: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0112 - acc: 0.5391 - val_loss: 0.0187 - val_acc: 0.6963
Epoch 14/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0115 - acc: 0.5562Epoch 00013: val_loss improved from 0.00947 to 0.00875, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0114 - acc: 0.5584 - val_loss: 0.0088 - val_acc: 0.6963
Epoch 15/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0103 - acc: 0.5814Epoch 00014: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0104 - acc: 0.5905 - val_loss: 0.0147 - val_acc: 0.6963
Epoch 16/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0096 - acc: 0.5962Epoch 00015: val_loss improved from 0.00875 to 0.00622, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0097 - acc: 0.5975 - val_loss: 0.0062 - val_acc: 0.6963
Epoch 17/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0096 - acc: 0.5814Epoch 00016: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0096 - acc: 0.5818 - val_loss: 0.0128 - val_acc: 0.6963
Epoch 18/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0085 - acc: 0.6007Epoch 00017: val_loss improved from 0.00622 to 0.00528, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0086 - acc: 0.5964 - val_loss: 0.0053 - val_acc: 0.6963
Epoch 19/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0083 - acc: 0.6025Epoch 00018: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0083 - acc: 0.6022 - val_loss: 0.0130 - val_acc: 0.6963
Epoch 20/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0087 - acc: 0.6079Epoch 00019: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0085 - acc: 0.6011 - val_loss: 0.0064 - val_acc: 0.6963
Epoch 21/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0078 - acc: 0.6114Epoch 00020: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0079 - acc: 0.6180 - val_loss: 0.0093 - val_acc: 0.6963
Epoch 22/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0072 - acc: 0.6171Epoch 00021: val_loss improved from 0.00528 to 0.00465, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0073 - acc: 0.6197 - val_loss: 0.0046 - val_acc: 0.6963
Epoch 23/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0076 - acc: 0.6243Epoch 00022: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0076 - acc: 0.6104 - val_loss: 0.0078 - val_acc: 0.6963
Epoch 24/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0068 - acc: 0.6257Epoch 00023: val_loss improved from 0.00465 to 0.00433, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0069 - acc: 0.6373 - val_loss: 0.0043 - val_acc: 0.6963
Epoch 25/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0068 - acc: 0.6507Epoch 00024: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0067 - acc: 0.6454 - val_loss: 0.0061 - val_acc: 0.6963
Epoch 26/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0072 - acc: 0.6429Epoch 00025: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0070 - acc: 0.6431 - val_loss: 0.0043 - val_acc: 0.6963
Epoch 27/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0065 - acc: 0.6619Epoch 00026: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0065 - acc: 0.6641 - val_loss: 0.0072 - val_acc: 0.6963
Epoch 28/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0067 - acc: 0.6643Epoch 00027: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0066 - acc: 0.6618 - val_loss: 0.0045 - val_acc: 0.6963
Epoch 29/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0065 - acc: 0.6593Epoch 00028: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0065 - acc: 0.6600 - val_loss: 0.0064 - val_acc: 0.6963
Epoch 30/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0060 - acc: 0.6686Epoch 00029: val_loss improved from 0.00433 to 0.00424, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0060 - acc: 0.6746 - val_loss: 0.0042 - val_acc: 0.6963
Epoch 31/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0065 - acc: 0.6500Epoch 00030: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0063 - acc: 0.6548 - val_loss: 0.0050 - val_acc: 0.6963
Epoch 32/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0061 - acc: 0.6694Epoch 00031: val_loss improved from 0.00424 to 0.00423, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0061 - acc: 0.6653 - val_loss: 0.0042 - val_acc: 0.6963
Epoch 33/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0056 - acc: 0.6507Epoch 00032: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0056 - acc: 0.6489 - val_loss: 0.0055 - val_acc: 0.6963
Epoch 34/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0065 - acc: 0.6686Epoch 00033: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0063 - acc: 0.6706 - val_loss: 0.0042 - val_acc: 0.6963
Epoch 35/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0057 - acc: 0.6825Epoch 00034: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0056 - acc: 0.6834 - val_loss: 0.0051 - val_acc: 0.6963
Epoch 36/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0057 - acc: 0.6794Epoch 00035: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0057 - acc: 0.6787 - val_loss: 0.0043 - val_acc: 0.6963
Epoch 37/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0056 - acc: 0.6821Epoch 00036: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0056 - acc: 0.6787 - val_loss: 0.0049 - val_acc: 0.6963
Epoch 38/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0055 - acc: 0.6807Epoch 00037: val_loss improved from 0.00423 to 0.00417, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0055 - acc: 0.6811 - val_loss: 0.0042 - val_acc: 0.6963
Epoch 39/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0053 - acc: 0.6862Epoch 00038: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0053 - acc: 0.6828 - val_loss: 0.0057 - val_acc: 0.6963
Epoch 40/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0055 - acc: 0.6643Epoch 00039: val_loss improved from 0.00417 to 0.00413, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0054 - acc: 0.6776 - val_loss: 0.0041 - val_acc: 0.6963
Epoch 41/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0054 - acc: 0.7031Epoch 00040: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0054 - acc: 0.6968 - val_loss: 0.0051 - val_acc: 0.6963
Epoch 42/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0053 - acc: 0.6964Epoch 00041: val_loss improved from 0.00413 to 0.00410, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0052 - acc: 0.6974 - val_loss: 0.0041 - val_acc: 0.6963
Epoch 43/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0051 - acc: 0.6971Epoch 00042: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0051 - acc: 0.6939 - val_loss: 0.0041 - val_acc: 0.6963
Epoch 44/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0053 - acc: 0.6944Epoch 00043: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0053 - acc: 0.6939 - val_loss: 0.0044 - val_acc: 0.6963
Epoch 45/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0048 - acc: 0.7000Epoch 00044: val_loss improved from 0.00410 to 0.00407, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0048 - acc: 0.6980 - val_loss: 0.0041 - val_acc: 0.6963
Epoch 46/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0051 - acc: 0.6864Epoch 00045: val_loss improved from 0.00407 to 0.00402, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0051 - acc: 0.6928 - val_loss: 0.0040 - val_acc: 0.6963
Epoch 47/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0050 - acc: 0.6971Epoch 00046: val_loss improved from 0.00402 to 0.00394, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0049 - acc: 0.7021 - val_loss: 0.0039 - val_acc: 0.6963
Epoch 48/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0049 - acc: 0.6950Epoch 00047: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0048 - acc: 0.6968 - val_loss: 0.0040 - val_acc: 0.6963
Epoch 49/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0050 - acc: 0.6993Epoch 00048: val_loss improved from 0.00394 to 0.00393, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0049 - acc: 0.6992 - val_loss: 0.0039 - val_acc: 0.6963
Epoch 50/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0049 - acc: 0.7037Epoch 00049: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0049 - acc: 0.7027 - val_loss: 0.0042 - val_acc: 0.6963
Epoch 51/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0047 - acc: 0.6950Epoch 00050: val_loss improved from 0.00393 to 0.00374, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0048 - acc: 0.6968 - val_loss: 0.0037 - val_acc: 0.6963
Epoch 52/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0046 - acc: 0.7129Epoch 00051: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0046 - acc: 0.7097 - val_loss: 0.0043 - val_acc: 0.6963
Epoch 53/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0044 - acc: 0.7064Epoch 00052: val_loss improved from 0.00374 to 0.00365, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0045 - acc: 0.7039 - val_loss: 0.0036 - val_acc: 0.6963
Epoch 54/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0046 - acc: 0.6950Epoch 00053: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0046 - acc: 0.6980 - val_loss: 0.0040 - val_acc: 0.6963
Epoch 55/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0046 - acc: 0.6950Epoch 00054: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0046 - acc: 0.6951 - val_loss: 0.0040 - val_acc: 0.6963
Epoch 56/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0043 - acc: 0.7071Epoch 00055: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0043 - acc: 0.7004 - val_loss: 0.0037 - val_acc: 0.6963
Epoch 57/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0043 - acc: 0.7036Epoch 00056: val_loss improved from 0.00365 to 0.00337, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0043 - acc: 0.7062 - val_loss: 0.0034 - val_acc: 0.6963
Epoch 58/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0042 - acc: 0.7050Epoch 00057: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0043 - acc: 0.7085 - val_loss: 0.0046 - val_acc: 0.6963
Epoch 59/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0040 - acc: 0.6971Epoch 00058: val_loss improved from 0.00337 to 0.00327, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0041 - acc: 0.7044 - val_loss: 0.0033 - val_acc: 0.7009
Epoch 60/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0041 - acc: 0.7029Epoch 00059: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0042 - acc: 0.7015 - val_loss: 0.0041 - val_acc: 0.6963
Epoch 61/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0040 - acc: 0.7036Epoch 00060: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0040 - acc: 0.7062 - val_loss: 0.0033 - val_acc: 0.6963
Epoch 62/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0040 - acc: 0.6986Epoch 00061: val_loss improved from 0.00327 to 0.00321, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0040 - acc: 0.7027 - val_loss: 0.0032 - val_acc: 0.7009
Epoch 63/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0041 - acc: 0.7000Epoch 00062: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0040 - acc: 0.7074 - val_loss: 0.0035 - val_acc: 0.6986
Epoch 64/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0037 - acc: 0.7050Epoch 00063: val_loss improved from 0.00321 to 0.00318, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0038 - acc: 0.7009 - val_loss: 0.0032 - val_acc: 0.7009
Epoch 65/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0039 - acc: 0.7094Epoch 00064: val_loss improved from 0.00318 to 0.00303, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0039 - acc: 0.7091 - val_loss: 0.0030 - val_acc: 0.7033
Epoch 66/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0037 - acc: 0.7012Epoch 00065: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0037 - acc: 0.7004 - val_loss: 0.0033 - val_acc: 0.6986
Epoch 67/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0036 - acc: 0.7021Epoch 00066: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0037 - acc: 0.7027 - val_loss: 0.0036 - val_acc: 0.6963
Epoch 68/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0037 - acc: 0.7186Epoch 00067: val_loss improved from 0.00303 to 0.00285, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0037 - acc: 0.7120 - val_loss: 0.0029 - val_acc: 0.7009
Epoch 69/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0037 - acc: 0.7000Epoch 00068: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0036 - acc: 0.7085 - val_loss: 0.0034 - val_acc: 0.7009
Epoch 70/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0034 - acc: 0.7129Epoch 00069: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0035 - acc: 0.7027 - val_loss: 0.0040 - val_acc: 0.6963
Epoch 71/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0034 - acc: 0.7094Epoch 00070: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0035 - acc: 0.7085 - val_loss: 0.0037 - val_acc: 0.6963
Epoch 72/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0036 - acc: 0.6964Epoch 00071: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0036 - acc: 0.7062 - val_loss: 0.0030 - val_acc: 0.7033
Epoch 73/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0033 - acc: 0.7121Epoch 00072: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0034 - acc: 0.7062 - val_loss: 0.0035 - val_acc: 0.6986
Epoch 74/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0032 - acc: 0.7112Epoch 00073: val_loss improved from 0.00285 to 0.00283, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0032 - acc: 0.7126 - val_loss: 0.0028 - val_acc: 0.7009
Epoch 75/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0036 - acc: 0.7221Epoch 00074: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0036 - acc: 0.7208 - val_loss: 0.0030 - val_acc: 0.7009
Epoch 76/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0032 - acc: 0.7157Epoch 00075: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0032 - acc: 0.7120 - val_loss: 0.0031 - val_acc: 0.7033
Epoch 77/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0034 - acc: 0.7236Epoch 00076: val_loss improved from 0.00283 to 0.00260, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0033 - acc: 0.7231 - val_loss: 0.0026 - val_acc: 0.7033
Epoch 78/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0033 - acc: 0.7194Epoch 00077: val_loss improved from 0.00260 to 0.00253, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0033 - acc: 0.7220 - val_loss: 0.0025 - val_acc: 0.7009
Epoch 79/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0031 - acc: 0.7100Epoch 00078: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0031 - acc: 0.7161 - val_loss: 0.0026 - val_acc: 0.7033
Epoch 80/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0031 - acc: 0.7137Epoch 00079: val_loss improved from 0.00253 to 0.00227, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0032 - acc: 0.7144 - val_loss: 0.0023 - val_acc: 0.7056
Epoch 81/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0032 - acc: 0.7114Epoch 00080: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0032 - acc: 0.7179 - val_loss: 0.0033 - val_acc: 0.7009
Epoch 82/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0030 - acc: 0.7121Epoch 00081: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0031 - acc: 0.7144 - val_loss: 0.0026 - val_acc: 0.7033
Epoch 83/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0030 - acc: 0.7306Epoch 00082: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0030 - acc: 0.7284 - val_loss: 0.0024 - val_acc: 0.6986
Epoch 84/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0030 - acc: 0.7143Epoch 00083: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0030 - acc: 0.7103 - val_loss: 0.0023 - val_acc: 0.6986
Epoch 85/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0029 - acc: 0.7214Epoch 00084: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0029 - acc: 0.7296 - val_loss: 0.0025 - val_acc: 0.7033
Epoch 86/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0031 - acc: 0.7081Epoch 00085: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0031 - acc: 0.7085 - val_loss: 0.0024 - val_acc: 0.7009
Epoch 87/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0029 - acc: 0.7264Epoch 00086: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0029 - acc: 0.7261 - val_loss: 0.0026 - val_acc: 0.7056
Epoch 88/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0028 - acc: 0.7243Epoch 00087: val_loss improved from 0.00227 to 0.00224, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0028 - acc: 0.7167 - val_loss: 0.0022 - val_acc: 0.7033
Epoch 89/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0029 - acc: 0.7121Epoch 00088: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0029 - acc: 0.7225 - val_loss: 0.0023 - val_acc: 0.7079
Epoch 90/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0029 - acc: 0.7279Epoch 00089: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0028 - acc: 0.7196 - val_loss: 0.0025 - val_acc: 0.7009
Epoch 91/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0031 - acc: 0.7119Epoch 00090: val_loss improved from 0.00224 to 0.00212, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0031 - acc: 0.7155 - val_loss: 0.0021 - val_acc: 0.6963
Epoch 92/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0027 - acc: 0.7294Epoch 00091: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0027 - acc: 0.7296 - val_loss: 0.0027 - val_acc: 0.7103
Epoch 93/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0031 - acc: 0.7107Epoch 00092: val_loss improved from 0.00212 to 0.00205, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0030 - acc: 0.7202 - val_loss: 0.0020 - val_acc: 0.7079
Epoch 94/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0028 - acc: 0.7250Epoch 00093: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0028 - acc: 0.7144 - val_loss: 0.0023 - val_acc: 0.7033
Epoch 95/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0026 - acc: 0.7293Epoch 00094: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7202 - val_loss: 0.0022 - val_acc: 0.6963
Epoch 96/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0027 - acc: 0.7225Epoch 00095: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0027 - acc: 0.7261 - val_loss: 0.0031 - val_acc: 0.7033
Epoch 97/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0027 - acc: 0.7179Epoch 00096: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0028 - acc: 0.7190 - val_loss: 0.0023 - val_acc: 0.7033
Epoch 98/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0026 - acc: 0.7419Epoch 00097: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7389 - val_loss: 0.0023 - val_acc: 0.7009
Epoch 99/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0030 - acc: 0.7343Epoch 00098: val_loss improved from 0.00205 to 0.00196, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0029 - acc: 0.7313 - val_loss: 0.0020 - val_acc: 0.7056
Epoch 100/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0026 - acc: 0.7362Epoch 00099: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7371 - val_loss: 0.0023 - val_acc: 0.7196
Epoch 101/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0026 - acc: 0.7393Epoch 00100: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7395 - val_loss: 0.0020 - val_acc: 0.7196
Epoch 102/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0025 - acc: 0.7362Epoch 00101: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0025 - acc: 0.7412 - val_loss: 0.0022 - val_acc: 0.6986
Epoch 103/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0025 - acc: 0.7371Epoch 00102: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7307 - val_loss: 0.0022 - val_acc: 0.6963
Epoch 104/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0027 - acc: 0.7336Epoch 00103: val_loss improved from 0.00196 to 0.00190, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7336 - val_loss: 0.0019 - val_acc: 0.7079
Epoch 105/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0025 - acc: 0.7250Epoch 00104: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0025 - acc: 0.7301 - val_loss: 0.0021 - val_acc: 0.7033
Epoch 106/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0025 - acc: 0.7329Epoch 00105: val_loss improved from 0.00190 to 0.00174, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7237 - val_loss: 0.0017 - val_acc: 0.7196
Epoch 107/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0026 - acc: 0.7381Epoch 00106: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7377 - val_loss: 0.0018 - val_acc: 0.7126
Epoch 108/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0024 - acc: 0.7375Epoch 00107: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7383 - val_loss: 0.0018 - val_acc: 0.7126
Epoch 109/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0024 - acc: 0.7307Epoch 00108: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0025 - acc: 0.7348 - val_loss: 0.0023 - val_acc: 0.7103
Epoch 110/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0025 - acc: 0.7350Epoch 00109: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0025 - acc: 0.7366 - val_loss: 0.0019 - val_acc: 0.7079
Epoch 111/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0023 - acc: 0.7543Epoch 00110: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7541 - val_loss: 0.0023 - val_acc: 0.7079
Epoch 112/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0026 - acc: 0.7275Epoch 00111: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0025 - acc: 0.7284 - val_loss: 0.0019 - val_acc: 0.7173
Epoch 113/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0024 - acc: 0.7321Epoch 00112: val_loss improved from 0.00174 to 0.00171, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7290 - val_loss: 0.0017 - val_acc: 0.7150
Epoch 114/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0023 - acc: 0.7364Epoch 00113: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0023 - acc: 0.7383 - val_loss: 0.0021 - val_acc: 0.7173
Epoch 115/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0025 - acc: 0.7471Epoch 00114: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0026 - acc: 0.7447 - val_loss: 0.0018 - val_acc: 0.7150
Epoch 116/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0022 - acc: 0.7364Epoch 00115: val_loss improved from 0.00171 to 0.00168, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0022 - acc: 0.7331 - val_loss: 0.0017 - val_acc: 0.7220
Epoch 117/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0024 - acc: 0.7271Epoch 00116: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7237 - val_loss: 0.0017 - val_acc: 0.7196
Epoch 118/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0022 - acc: 0.7225Epoch 00117: val_loss improved from 0.00168 to 0.00156, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0022 - acc: 0.7261 - val_loss: 0.0016 - val_acc: 0.7360
Epoch 119/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0026 - acc: 0.7350Epoch 00118: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0025 - acc: 0.7348 - val_loss: 0.0019 - val_acc: 0.7150
Epoch 120/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0022 - acc: 0.7450Epoch 00119: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0022 - acc: 0.7442 - val_loss: 0.0019 - val_acc: 0.7126
Epoch 121/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0024 - acc: 0.7486Epoch 00120: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7442 - val_loss: 0.0023 - val_acc: 0.7266
Epoch 122/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0023 - acc: 0.7300Epoch 00121: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0022 - acc: 0.7354 - val_loss: 0.0017 - val_acc: 0.7126
Epoch 123/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0022 - acc: 0.7350Epoch 00122: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0023 - acc: 0.7389 - val_loss: 0.0017 - val_acc: 0.7126
Epoch 124/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0023 - acc: 0.7436Epoch 00123: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0023 - acc: 0.7360 - val_loss: 0.0016 - val_acc: 0.7290
Epoch 125/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0024 - acc: 0.7250Epoch 00124: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7290 - val_loss: 0.0017 - val_acc: 0.7126
Epoch 126/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0022 - acc: 0.7443Epoch 00125: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7512 - val_loss: 0.0017 - val_acc: 0.7196
Epoch 127/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0023 - acc: 0.7356Epoch 00126: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0023 - acc: 0.7377 - val_loss: 0.0016 - val_acc: 0.7243
Epoch 128/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0022 - acc: 0.7394Epoch 00127: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0022 - acc: 0.7389 - val_loss: 0.0017 - val_acc: 0.7290
Epoch 129/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0021 - acc: 0.7350Epoch 00128: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7465 - val_loss: 0.0019 - val_acc: 0.7173
Epoch 130/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0023 - acc: 0.7457Epoch 00129: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0023 - acc: 0.7442 - val_loss: 0.0020 - val_acc: 0.7173
Epoch 131/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0021 - acc: 0.7364Epoch 00130: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7477 - val_loss: 0.0018 - val_acc: 0.7196
Epoch 132/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0020 - acc: 0.7487Epoch 00131: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7453 - val_loss: 0.0024 - val_acc: 0.7196
Epoch 133/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0023 - acc: 0.7500Epoch 00132: val_loss improved from 0.00156 to 0.00149, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0023 - acc: 0.7401 - val_loss: 0.0015 - val_acc: 0.7313
Epoch 134/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0020 - acc: 0.7393Epoch 00133: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7412 - val_loss: 0.0016 - val_acc: 0.7266
Epoch 135/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0022 - acc: 0.7436Epoch 00134: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0023 - acc: 0.7407 - val_loss: 0.0019 - val_acc: 0.7103
Epoch 136/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0021 - acc: 0.7579Epoch 00135: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7518 - val_loss: 0.0016 - val_acc: 0.7196
Epoch 137/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0020 - acc: 0.7529Epoch 00136: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7459 - val_loss: 0.0016 - val_acc: 0.7313
Epoch 138/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7329Epoch 00137: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7412 - val_loss: 0.0021 - val_acc: 0.7407
Epoch 139/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0026 - acc: 0.7493Epoch 00138: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7447 - val_loss: 0.0015 - val_acc: 0.7243
Epoch 140/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0019 - acc: 0.7525Epoch 00139: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7523 - val_loss: 0.0018 - val_acc: 0.7103
Epoch 141/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0020 - acc: 0.7429Epoch 00140: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7430 - val_loss: 0.0017 - val_acc: 0.7220
Epoch 142/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0021 - acc: 0.7519Epoch 00141: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7535 - val_loss: 0.0016 - val_acc: 0.7150
Epoch 143/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0020 - acc: 0.7500Epoch 00142: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7465 - val_loss: 0.0016 - val_acc: 0.7313
Epoch 144/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0021 - acc: 0.7443Epoch 00143: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7523 - val_loss: 0.0017 - val_acc: 0.7150
Epoch 145/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7514Epoch 00144: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7447 - val_loss: 0.0018 - val_acc: 0.7243
Epoch 146/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0021 - acc: 0.7607Epoch 00145: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0021 - acc: 0.7617 - val_loss: 0.0020 - val_acc: 0.7103
Epoch 147/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0019 - acc: 0.7450Epoch 00146: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7482 - val_loss: 0.0016 - val_acc: 0.7290
Epoch 148/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0019 - acc: 0.7612Epoch 00147: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7652 - val_loss: 0.0017 - val_acc: 0.7407
Epoch 149/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7421Epoch 00148: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7465 - val_loss: 0.0016 - val_acc: 0.7220
Epoch 150/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7579Epoch 00149: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7564 - val_loss: 0.0021 - val_acc: 0.7079
Epoch 151/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0025 - acc: 0.7479Epoch 00150: val_loss improved from 0.00149 to 0.00148, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0024 - acc: 0.7477 - val_loss: 0.0015 - val_acc: 0.7360
Epoch 152/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7479Epoch 00151: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7518 - val_loss: 0.0016 - val_acc: 0.7383
Epoch 153/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7607Epoch 00152: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7652 - val_loss: 0.0016 - val_acc: 0.7243
Epoch 154/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0020 - acc: 0.7450Epoch 00153: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7512 - val_loss: 0.0016 - val_acc: 0.7290
Epoch 155/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0019 - acc: 0.7400Epoch 00154: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7436 - val_loss: 0.0015 - val_acc: 0.7290
Epoch 156/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0021 - acc: 0.7586Epoch 00155: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0020 - acc: 0.7529 - val_loss: 0.0017 - val_acc: 0.7266
Epoch 157/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7514Epoch 00156: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7582 - val_loss: 0.0018 - val_acc: 0.7336
Epoch 158/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0019 - acc: 0.7631Epoch 00157: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7634 - val_loss: 0.0016 - val_acc: 0.7336
Epoch 159/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7379Epoch 00158: val_loss improved from 0.00148 to 0.00142, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7494 - val_loss: 0.0014 - val_acc: 0.7360
Epoch 160/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7450Epoch 00159: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7506 - val_loss: 0.0021 - val_acc: 0.7407
Epoch 161/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0019 - acc: 0.7662Epoch 00160: val_loss improved from 0.00142 to 0.00139, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7658 - val_loss: 0.0014 - val_acc: 0.7477
Epoch 162/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0019 - acc: 0.7525Epoch 00161: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7500 - val_loss: 0.0016 - val_acc: 0.7266
Epoch 163/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7557Epoch 00162: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7494 - val_loss: 0.0015 - val_acc: 0.7477
Epoch 164/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7793Epoch 00163: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7734 - val_loss: 0.0018 - val_acc: 0.7150
Epoch 165/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7657Epoch 00164: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7693 - val_loss: 0.0014 - val_acc: 0.7453
Epoch 166/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0017 - acc: 0.7456Epoch 00165: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7430 - val_loss: 0.0020 - val_acc: 0.7220
Epoch 167/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7571Epoch 00166: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0019 - acc: 0.7576 - val_loss: 0.0015 - val_acc: 0.7407
Epoch 168/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7679Epoch 00167: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7646 - val_loss: 0.0014 - val_acc: 0.7453
Epoch 169/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7650Epoch 00168: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7681 - val_loss: 0.0014 - val_acc: 0.7523
Epoch 170/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7536Epoch 00169: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7529 - val_loss: 0.0015 - val_acc: 0.7523
Epoch 171/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7707Epoch 00170: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7716 - val_loss: 0.0018 - val_acc: 0.7407
Epoch 172/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7621Epoch 00171: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7558 - val_loss: 0.0016 - val_acc: 0.7383
Epoch 173/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7507Epoch 00172: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7553 - val_loss: 0.0017 - val_acc: 0.7336
Epoch 174/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7707Epoch 00173: val_loss improved from 0.00139 to 0.00137, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7617 - val_loss: 0.0014 - val_acc: 0.7407
Epoch 175/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7736Epoch 00174: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7704 - val_loss: 0.0016 - val_acc: 0.7617
Epoch 176/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7564Epoch 00175: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7558 - val_loss: 0.0015 - val_acc: 0.7383
Epoch 177/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7686Epoch 00176: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7634 - val_loss: 0.0014 - val_acc: 0.7290
Epoch 178/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7536Epoch 00177: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7529 - val_loss: 0.0020 - val_acc: 0.7523
Epoch 179/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0018 - acc: 0.7594Epoch 00178: val_loss improved from 0.00137 to 0.00133, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7623 - val_loss: 0.0013 - val_acc: 0.7360
Epoch 180/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0016 - acc: 0.7675Epoch 00179: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7687 - val_loss: 0.0019 - val_acc: 0.7453
Epoch 181/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7607Epoch 00180: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7623 - val_loss: 0.0014 - val_acc: 0.7407
Epoch 182/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7657Epoch 00181: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7716 - val_loss: 0.0015 - val_acc: 0.7523
Epoch 183/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0018 - acc: 0.7600Epoch 00182: val_loss improved from 0.00133 to 0.00130, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7605 - val_loss: 0.0013 - val_acc: 0.7477
Epoch 184/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7621Epoch 00183: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7582 - val_loss: 0.0015 - val_acc: 0.7500
Epoch 185/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7771Epoch 00184: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7780 - val_loss: 0.0015 - val_acc: 0.7430
Epoch 186/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7621Epoch 00185: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7675 - val_loss: 0.0014 - val_acc: 0.7313
Epoch 187/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7657Epoch 00186: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7716 - val_loss: 0.0014 - val_acc: 0.7593
Epoch 188/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7679Epoch 00187: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7687 - val_loss: 0.0014 - val_acc: 0.7360
Epoch 189/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7521Epoch 00188: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7535 - val_loss: 0.0019 - val_acc: 0.7453
Epoch 190/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7750Epoch 00189: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0017 - acc: 0.7704 - val_loss: 0.0015 - val_acc: 0.7500
Epoch 191/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7750Epoch 00190: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7710 - val_loss: 0.0014 - val_acc: 0.7617
Epoch 192/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0017 - acc: 0.7550Epoch 00191: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7582 - val_loss: 0.0014 - val_acc: 0.7477
Epoch 193/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0015 - acc: 0.7869Epoch 00192: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7868 - val_loss: 0.0018 - val_acc: 0.7313
Epoch 194/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0019 - acc: 0.7707Epoch 00193: val_loss improved from 0.00130 to 0.00129, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0018 - acc: 0.7745 - val_loss: 0.0013 - val_acc: 0.7593
Epoch 195/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7764Epoch 00194: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7850 - val_loss: 0.0013 - val_acc: 0.7500
Epoch 196/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7664Epoch 00195: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7699 - val_loss: 0.0014 - val_acc: 0.7500
Epoch 197/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7736Epoch 00196: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7780 - val_loss: 0.0017 - val_acc: 0.7336
Epoch 198/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0015 - acc: 0.7863Epoch 00197: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7856 - val_loss: 0.0017 - val_acc: 0.7453
Epoch 199/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7693Epoch 00198: val_loss improved from 0.00129 to 0.00128, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7710 - val_loss: 0.0013 - val_acc: 0.7523
Epoch 200/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7821Epoch 00199: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7798 - val_loss: 0.0017 - val_acc: 0.7523
Epoch 201/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0015 - acc: 0.7744Epoch 00200: val_loss improved from 0.00128 to 0.00125, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7739 - val_loss: 0.0012 - val_acc: 0.7734
Epoch 202/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0015 - acc: 0.7631Epoch 00201: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7658 - val_loss: 0.0016 - val_acc: 0.7570
Epoch 203/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7607Epoch 00202: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7652 - val_loss: 0.0013 - val_acc: 0.7664
Epoch 204/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7679Epoch 00203: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7751 - val_loss: 0.0014 - val_acc: 0.7360
Epoch 205/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7707Epoch 00204: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7763 - val_loss: 0.0017 - val_acc: 0.7547
Epoch 206/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7814Epoch 00205: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7827 - val_loss: 0.0013 - val_acc: 0.7547
Epoch 207/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7771Epoch 00206: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7699 - val_loss: 0.0015 - val_acc: 0.7523
Epoch 208/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7850Epoch 00207: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7827 - val_loss: 0.0013 - val_acc: 0.7547
Epoch 209/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7779Epoch 00208: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7780 - val_loss: 0.0015 - val_acc: 0.7570
Epoch 210/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7729Epoch 00209: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7687 - val_loss: 0.0014 - val_acc: 0.7500
Epoch 211/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7593Epoch 00210: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0016 - acc: 0.7664 - val_loss: 0.0013 - val_acc: 0.7687
Epoch 212/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7843Epoch 00211: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7821 - val_loss: 0.0013 - val_acc: 0.7547
Epoch 213/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0016 - acc: 0.7650Epoch 00212: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7681 - val_loss: 0.0013 - val_acc: 0.7570
Epoch 214/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7864Epoch 00213: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7804 - val_loss: 0.0014 - val_acc: 0.7570
Epoch 215/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7750Epoch 00214: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7769 - val_loss: 0.0014 - val_acc: 0.7523
Epoch 216/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7686Epoch 00215: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7751 - val_loss: 0.0013 - val_acc: 0.7617
Epoch 217/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7679Epoch 00216: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7739 - val_loss: 0.0013 - val_acc: 0.7523
Epoch 218/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7764Epoch 00217: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7792 - val_loss: 0.0014 - val_acc: 0.7734
Epoch 219/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7836Epoch 00218: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7886 - val_loss: 0.0013 - val_acc: 0.7547
Epoch 220/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7914Epoch 00219: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7839 - val_loss: 0.0013 - val_acc: 0.7687
Epoch 221/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0015 - acc: 0.7831Epoch 00220: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7845 - val_loss: 0.0018 - val_acc: 0.7664
Epoch 222/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7821Epoch 00221: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7926 - val_loss: 0.0013 - val_acc: 0.7734
Epoch 223/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7857Epoch 00222: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7827 - val_loss: 0.0013 - val_acc: 0.7547
Epoch 224/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7850Epoch 00223: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7839 - val_loss: 0.0016 - val_acc: 0.7687
Epoch 225/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7864Epoch 00224: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0015 - acc: 0.7780 - val_loss: 0.0013 - val_acc: 0.7617
Epoch 226/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7750Epoch 00225: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7786 - val_loss: 0.0013 - val_acc: 0.7734
Epoch 227/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7729Epoch 00226: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7798 - val_loss: 0.0014 - val_acc: 0.7593
Epoch 228/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7807Epoch 00227: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7845 - val_loss: 0.0014 - val_acc: 0.7757
Epoch 229/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7721Epoch 00228: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7757 - val_loss: 0.0013 - val_acc: 0.7593
Epoch 230/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7764Epoch 00229: val_loss improved from 0.00125 to 0.00123, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7769 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 231/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0014 - acc: 0.7806Epoch 00230: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7827 - val_loss: 0.0013 - val_acc: 0.7617
Epoch 232/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0013 - acc: 0.7825Epoch 00231: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7798 - val_loss: 0.0014 - val_acc: 0.7710
Epoch 233/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0014 - acc: 0.7756Epoch 00232: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7775 - val_loss: 0.0013 - val_acc: 0.7570
Epoch 234/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7743Epoch 00233: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7751 - val_loss: 0.0012 - val_acc: 0.7710
Epoch 235/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7886Epoch 00234: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7874 - val_loss: 0.0013 - val_acc: 0.7687
Epoch 236/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7814Epoch 00235: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7850 - val_loss: 0.0013 - val_acc: 0.7757
Epoch 237/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7743Epoch 00236: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7751 - val_loss: 0.0015 - val_acc: 0.7547
Epoch 238/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0014 - acc: 0.7871Epoch 00237: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7862 - val_loss: 0.0014 - val_acc: 0.7664
Epoch 239/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7764Epoch 00238: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7821 - val_loss: 0.0014 - val_acc: 0.7617
Epoch 240/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0015 - acc: 0.7736Epoch 00239: val_loss improved from 0.00123 to 0.00120, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0014 - acc: 0.7769 - val_loss: 0.0012 - val_acc: 0.7640
Epoch 241/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7957Epoch 00240: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7938 - val_loss: 0.0012 - val_acc: 0.7734
Epoch 242/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7771Epoch 00241: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7769 - val_loss: 0.0013 - val_acc: 0.7687
Epoch 243/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7829Epoch 00242: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7815 - val_loss: 0.0013 - val_acc: 0.7523
Epoch 244/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7893Epoch 00243: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7891 - val_loss: 0.0016 - val_acc: 0.7710
Epoch 245/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7871Epoch 00244: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7897 - val_loss: 0.0013 - val_acc: 0.7710
Epoch 246/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.8000Epoch 00245: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7961 - val_loss: 0.0014 - val_acc: 0.7897
Epoch 247/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7950Epoch 00246: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7938 - val_loss: 0.0013 - val_acc: 0.7780
Epoch 248/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7900Epoch 00247: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7944 - val_loss: 0.0014 - val_acc: 0.7757
Epoch 249/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7771Epoch 00248: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7833 - val_loss: 0.0014 - val_acc: 0.7780
Epoch 250/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7821Epoch 00249: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7897 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 251/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0013 - acc: 0.7794Epoch 00250: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7780 - val_loss: 0.0012 - val_acc: 0.7687
Epoch 252/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7871Epoch 00251: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7903 - val_loss: 0.0012 - val_acc: 0.7780
Epoch 253/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7671Epoch 00252: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7669 - val_loss: 0.0013 - val_acc: 0.7850
Epoch 254/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0013 - acc: 0.8019Epoch 00253: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.8008 - val_loss: 0.0013 - val_acc: 0.7804
Epoch 255/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0012 - acc: 0.7963Epoch 00254: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7956 - val_loss: 0.0015 - val_acc: 0.7757
Epoch 256/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7843Epoch 00255: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7862 - val_loss: 0.0012 - val_acc: 0.7570
Epoch 257/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0012 - acc: 0.8038Epoch 00256: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.8032 - val_loss: 0.0013 - val_acc: 0.7664
Epoch 258/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7900Epoch 00257: val_loss improved from 0.00120 to 0.00117, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7880 - val_loss: 0.0012 - val_acc: 0.7780
Epoch 259/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7786Epoch 00258: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7815 - val_loss: 0.0014 - val_acc: 0.7664
Epoch 260/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0012 - acc: 0.7994Epoch 00259: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7979 - val_loss: 0.0013 - val_acc: 0.7687
Epoch 261/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7814Epoch 00260: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7868 - val_loss: 0.0013 - val_acc: 0.7734
Epoch 262/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7900Epoch 00261: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7921 - val_loss: 0.0012 - val_acc: 0.7850
Epoch 263/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7893Epoch 00262: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7880 - val_loss: 0.0012 - val_acc: 0.7850
Epoch 264/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7971Epoch 00263: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7956 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 265/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7964Epoch 00264: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.8002 - val_loss: 0.0013 - val_acc: 0.7687
Epoch 266/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0013 - acc: 0.7986Epoch 00265: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0013 - acc: 0.7909 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 267/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7943Epoch 00266: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7897 - val_loss: 0.0015 - val_acc: 0.7757
Epoch 268/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7921Epoch 00267: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7874 - val_loss: 0.0014 - val_acc: 0.7710
Epoch 269/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7836Epoch 00268: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7903 - val_loss: 0.0012 - val_acc: 0.7734
Epoch 270/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7950Epoch 00269: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7956 - val_loss: 0.0012 - val_acc: 0.7640
Epoch 271/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7929Epoch 00270: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7921 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 272/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7950Epoch 00271: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7979 - val_loss: 0.0014 - val_acc: 0.7780
Epoch 273/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7850Epoch 00272: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7880 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 274/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7921Epoch 00273: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7909 - val_loss: 0.0014 - val_acc: 0.7804
Epoch 275/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7957Epoch 00274: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.8014 - val_loss: 0.0013 - val_acc: 0.7757
Epoch 276/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7836Epoch 00275: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7862 - val_loss: 0.0013 - val_acc: 0.7710
Epoch 277/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.7937Epoch 00276: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7961 - val_loss: 0.0014 - val_acc: 0.7921
Epoch 278/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7807Epoch 00277: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7810 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 279/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.7929Epoch 00278: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7938 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 280/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.8043Epoch 00279: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7985 - val_loss: 0.0012 - val_acc: 0.7944
Epoch 281/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0012 - acc: 0.7969Epoch 00280: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7950 - val_loss: 0.0013 - val_acc: 0.7757
Epoch 282/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.7937Epoch 00281: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7979 - val_loss: 0.0012 - val_acc: 0.7617
Epoch 283/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7793Epoch 00282: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7868 - val_loss: 0.0013 - val_acc: 0.7757
Epoch 284/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0012 - acc: 0.7963Epoch 00283: val_loss improved from 0.00117 to 0.00114, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7973 - val_loss: 0.0011 - val_acc: 0.7874
Epoch 285/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7886Epoch 00284: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7985 - val_loss: 0.0013 - val_acc: 0.7921
Epoch 286/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7964Epoch 00285: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7903 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 287/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7979Epoch 00286: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.8014 - val_loss: 0.0013 - val_acc: 0.7664
Epoch 288/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.8000Epoch 00287: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7967 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 289/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.7894 Epoch 00288: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.7926 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 290/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.8200Epoch 00289: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.8172 - val_loss: 0.0012 - val_acc: 0.7687
Epoch 291/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7929Epoch 00290: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7973 - val_loss: 0.0014 - val_acc: 0.7827
Epoch 292/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0012 - acc: 0.8086Epoch 00291: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.8043 - val_loss: 0.0013 - val_acc: 0.7734
Epoch 293/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7943Epoch 00292: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7956 - val_loss: 0.0014 - val_acc: 0.7780
Epoch 294/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.8029Epoch 00293: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.8014 - val_loss: 0.0012 - val_acc: 0.7734
Epoch 295/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.7919Epoch 00294: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7956 - val_loss: 0.0013 - val_acc: 0.7664
Epoch 296/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.8100Epoch 00295: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.8067 - val_loss: 0.0013 - val_acc: 0.7757
Epoch 297/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.7956Epoch 00296: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7956 - val_loss: 0.0013 - val_acc: 0.7640
Epoch 298/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.8014Epoch 00297: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7967 - val_loss: 0.0013 - val_acc: 0.7757
Epoch 299/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7921Epoch 00298: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7944 - val_loss: 0.0014 - val_acc: 0.7664
Epoch 300/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.8019Epoch 00299: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.8049 - val_loss: 0.0012 - val_acc: 0.7734
Epoch 301/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.7979Epoch 00300: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.7979 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 302/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7871Epoch 00301: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7839 - val_loss: 0.0012 - val_acc: 0.7734
Epoch 303/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8093Epoch 00302: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8078 - val_loss: 0.0013 - val_acc: 0.7780
Epoch 304/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8021   Epoch 00303: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8002 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 305/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0012 - acc: 0.7919 Epoch 00304: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0012 - acc: 0.7915 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 306/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.7957  Epoch 00305: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.7926 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 307/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8064    Epoch 00306: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8061 - val_loss: 0.0012 - val_acc: 0.7734
Epoch 308/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7900Epoch 00307: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7932 - val_loss: 0.0013 - val_acc: 0.7921
Epoch 309/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8100Epoch 00308: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8049 - val_loss: 0.0012 - val_acc: 0.7687
Epoch 310/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8000Epoch 00309: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8014 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 311/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.7993   Epoch 00310: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7979 - val_loss: 0.0012 - val_acc: 0.7710
Epoch 312/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.8036Epoch 00311: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7991 - val_loss: 0.0012 - val_acc: 0.7710
Epoch 313/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8121Epoch 00312: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8096 - val_loss: 0.0012 - val_acc: 0.7710
Epoch 314/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7993Epoch 00313: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7938 - val_loss: 0.0012 - val_acc: 0.7874
Epoch 315/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0010 - acc: 0.8056Epoch 00314: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8049 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 316/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0010 - acc: 0.7956   Epoch 00315: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.7944 - val_loss: 0.0012 - val_acc: 0.8084
Epoch 317/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.9921e-04 - acc: 0.7971Epoch 00316: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.9959e-04 - acc: 0.8008 - val_loss: 0.0013 - val_acc: 0.7827
Epoch 318/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0010 - acc: 0.7975Epoch 00317: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.7991 - val_loss: 0.0013 - val_acc: 0.7757
Epoch 319/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.7979   Epoch 00318: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8020 - val_loss: 0.0012 - val_acc: 0.7687
Epoch 320/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8071Epoch 00319: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8014 - val_loss: 0.0013 - val_acc: 0.7897
Epoch 321/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.7979Epoch 00320: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0011 - acc: 0.7886 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 322/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.8086e-04 - acc: 0.7929Epoch 00321: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7169e-04 - acc: 0.7956 - val_loss: 0.0013 - val_acc: 0.7827
Epoch 323/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8014Epoch 00322: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.7979 - val_loss: 0.0013 - val_acc: 0.7874
Epoch 324/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8014Epoch 00323: val_loss improved from 0.00114 to 0.00111, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8026 - val_loss: 0.0011 - val_acc: 0.7874
Epoch 325/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.7971    Epoch 00324: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8008 - val_loss: 0.0012 - val_acc: 0.8131
Epoch 326/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.9923e-04 - acc: 0.8019Epoch 00325: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8008 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 327/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8136  Epoch 00326: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8131 - val_loss: 0.0012 - val_acc: 0.7921
Epoch 328/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0010 - acc: 0.7950 Epoch 00327: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.7991 - val_loss: 0.0012 - val_acc: 0.7780
Epoch 329/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.9086e-04 - acc: 0.7993Epoch 00328: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.9110e-04 - acc: 0.7944 - val_loss: 0.0011 - val_acc: 0.7967
Epoch 330/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.9562e-04 - acc: 0.8069Epoch 00329: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8084 - val_loss: 0.0012 - val_acc: 0.7921
Epoch 331/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8143Epoch 00330: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8084 - val_loss: 0.0014 - val_acc: 0.7640
Epoch 332/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.8097e-04 - acc: 0.8081Epoch 00331: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7739e-04 - acc: 0.8084 - val_loss: 0.0011 - val_acc: 0.7780
Epoch 333/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.8121   Epoch 00332: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8078 - val_loss: 0.0012 - val_acc: 0.7967
Epoch 334/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.6888e-04 - acc: 0.8021Epoch 00333: val_loss improved from 0.00111 to 0.00110, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 9.6057e-04 - acc: 0.8055 - val_loss: 0.0011 - val_acc: 0.7874
Epoch 335/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0011 - acc: 0.8036  Epoch 00334: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8067 - val_loss: 0.0013 - val_acc: 0.7944
Epoch 336/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.7985e-04 - acc: 0.8029Epoch 00335: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.8680e-04 - acc: 0.8008 - val_loss: 0.0014 - val_acc: 0.7687
Epoch 337/400
1400/1712 [=======================>......] - ETA: 0s - loss: 0.0010 - acc: 0.7979Epoch 00336: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.9418e-04 - acc: 0.8043 - val_loss: 0.0013 - val_acc: 0.8014
Epoch 338/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0010 - acc: 0.7981Epoch 00337: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8002 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 339/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.4972e-04 - acc: 0.8250Epoch 00338: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7014e-04 - acc: 0.8230 - val_loss: 0.0013 - val_acc: 0.7687
Epoch 340/400
1600/1712 [===========================>..] - ETA: 0s - loss: 0.0010 - acc: 0.8050 Epoch 00339: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 0.0010 - acc: 0.8055 - val_loss: 0.0011 - val_acc: 0.7874
Epoch 341/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.7513e-04 - acc: 0.8029Epoch 00340: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.5895e-04 - acc: 0.8078 - val_loss: 0.0013 - val_acc: 0.7967
Epoch 342/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.6390e-04 - acc: 0.7914Epoch 00341: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.5311e-04 - acc: 0.7950 - val_loss: 0.0012 - val_acc: 0.7921
Epoch 343/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.7656e-04 - acc: 0.8029Epoch 00342: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.5147e-04 - acc: 0.8037 - val_loss: 0.0011 - val_acc: 0.7850
Epoch 344/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.6802e-04 - acc: 0.8007Epoch 00343: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.9389e-04 - acc: 0.8014 - val_loss: 0.0014 - val_acc: 0.7827
Epoch 345/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.8069e-04 - acc: 0.8114Epoch 00344: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.6583e-04 - acc: 0.8119 - val_loss: 0.0012 - val_acc: 0.7921
Epoch 346/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.4762e-04 - acc: 0.7993Epoch 00345: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.5056e-04 - acc: 0.8055 - val_loss: 0.0013 - val_acc: 0.7780
Epoch 347/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.6365e-04 - acc: 0.8031Epoch 00346: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7690e-04 - acc: 0.8049 - val_loss: 0.0011 - val_acc: 0.7827
Epoch 348/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.7875e-04 - acc: 0.7925Epoch 00347: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7732e-04 - acc: 0.7985 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 349/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.4848e-04 - acc: 0.8093Epoch 00348: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.5213e-04 - acc: 0.8166 - val_loss: 0.0012 - val_acc: 0.8014
Epoch 350/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.7999e-04 - acc: 0.8264Epoch 00349: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7311e-04 - acc: 0.8213 - val_loss: 0.0011 - val_acc: 0.7804
Epoch 351/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.3896e-04 - acc: 0.8056Epoch 00350: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4020e-04 - acc: 0.8026 - val_loss: 0.0012 - val_acc: 0.7804
Epoch 352/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.2120e-04 - acc: 0.7993Epoch 00351: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.2916e-04 - acc: 0.7956 - val_loss: 0.0011 - val_acc: 0.8037
Epoch 353/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.8114e-04 - acc: 0.8106Epoch 00352: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7540e-04 - acc: 0.8148 - val_loss: 0.0012 - val_acc: 0.7780
Epoch 354/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.4656e-04 - acc: 0.7957Epoch 00353: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4073e-04 - acc: 0.7973 - val_loss: 0.0012 - val_acc: 0.7897
Epoch 355/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.2385e-04 - acc: 0.7921Epoch 00354: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.3529e-04 - acc: 0.7850 - val_loss: 0.0012 - val_acc: 0.7897
Epoch 356/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.4064e-04 - acc: 0.8200Epoch 00355: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4878e-04 - acc: 0.8143 - val_loss: 0.0012 - val_acc: 0.7967
Epoch 357/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.2636e-04 - acc: 0.8164Epoch 00356: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.3681e-04 - acc: 0.8131 - val_loss: 0.0012 - val_acc: 0.7897
Epoch 358/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.9662e-04 - acc: 0.8069Epoch 00357: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.9030e-04 - acc: 0.8078 - val_loss: 0.0012 - val_acc: 0.7944
Epoch 359/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.3538e-04 - acc: 0.8150Epoch 00358: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.3338e-04 - acc: 0.8055 - val_loss: 0.0012 - val_acc: 0.7640
Epoch 360/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.2655e-04 - acc: 0.8114Epoch 00359: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4185e-04 - acc: 0.8107 - val_loss: 0.0013 - val_acc: 0.7921
Epoch 361/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.7457e-04 - acc: 0.8219Epoch 00360: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7207e-04 - acc: 0.8236 - val_loss: 0.0012 - val_acc: 0.7921
Epoch 362/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.4607e-04 - acc: 0.8081Epoch 00361: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.3980e-04 - acc: 0.8096 - val_loss: 0.0011 - val_acc: 0.7944
Epoch 363/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.5558e-04 - acc: 0.8064Epoch 00362: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4429e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.7664
Epoch 364/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.5254e-04 - acc: 0.8186Epoch 00363: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4375e-04 - acc: 0.8254 - val_loss: 0.0011 - val_acc: 0.7944
Epoch 365/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.6219e-04 - acc: 0.8000Epoch 00364: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.5290e-04 - acc: 0.8061 - val_loss: 0.0013 - val_acc: 0.7897
Epoch 366/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.5114e-04 - acc: 0.8136Epoch 00365: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.2909e-04 - acc: 0.8183 - val_loss: 0.0011 - val_acc: 0.7921
Epoch 367/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.4345e-04 - acc: 0.8021Epoch 00366: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.3520e-04 - acc: 0.8049 - val_loss: 0.0013 - val_acc: 0.7944
Epoch 368/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.1716e-04 - acc: 0.8229Epoch 00367: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.1346e-04 - acc: 0.8271 - val_loss: 0.0012 - val_acc: 0.7874
Epoch 369/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.0086e-04 - acc: 0.8162Epoch 00368: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.9738e-04 - acc: 0.8148 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 370/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.8429e-04 - acc: 0.8029Epoch 00369: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.7456e-04 - acc: 0.8107 - val_loss: 0.0012 - val_acc: 0.7547
Epoch 371/400
1600/1712 [===========================>..] - ETA: 0s - loss: 8.8444e-04 - acc: 0.8156Epoch 00370: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.8323e-04 - acc: 0.8148 - val_loss: 0.0012 - val_acc: 0.7827
Epoch 372/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.5939e-04 - acc: 0.8219Epoch 00371: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.5457e-04 - acc: 0.8242 - val_loss: 0.0013 - val_acc: 0.7850
Epoch 373/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.2294e-04 - acc: 0.8021Epoch 00372: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.1791e-04 - acc: 0.8067 - val_loss: 0.0011 - val_acc: 0.7804
Epoch 374/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.9622e-04 - acc: 0.8079Epoch 00373: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.0194e-04 - acc: 0.8107 - val_loss: 0.0011 - val_acc: 0.7874
Epoch 375/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.1174e-04 - acc: 0.8264Epoch 00374: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.2162e-04 - acc: 0.8271 - val_loss: 0.0014 - val_acc: 0.7967
Epoch 376/400
1600/1712 [===========================>..] - ETA: 0s - loss: 8.9792e-04 - acc: 0.8100Epoch 00375: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.0395e-04 - acc: 0.8102 - val_loss: 0.0012 - val_acc: 0.7991
Epoch 377/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.4009e-04 - acc: 0.8093Epoch 00376: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.3016e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.7804
Epoch 378/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.1584e-04 - acc: 0.8071Epoch 00377: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.0674e-04 - acc: 0.8049 - val_loss: 0.0012 - val_acc: 0.8014
Epoch 379/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.0677e-04 - acc: 0.8125- ETA: 0s - loss: 9.1239e-04 - acc: 0.81Epoch 00378: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.0288e-04 - acc: 0.8125 - val_loss: 0.0012 - val_acc: 0.7991
Epoch 380/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.7237e-04 - acc: 0.8150Epoch 00379: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.7336e-04 - acc: 0.8189 - val_loss: 0.0011 - val_acc: 0.7780
Epoch 381/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.2184e-04 - acc: 0.8164Epoch 00380: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.1442e-04 - acc: 0.8224 - val_loss: 0.0012 - val_acc: 0.7897
Epoch 382/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.1497e-04 - acc: 0.8114Epoch 00381: val_loss improved from 0.00110 to 0.00110, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 9.0897e-04 - acc: 0.8143 - val_loss: 0.0011 - val_acc: 0.7897
Epoch 383/400
1600/1712 [===========================>..] - ETA: 0s - loss: 8.7441e-04 - acc: 0.8219Epoch 00382: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.7454e-04 - acc: 0.8236 - val_loss: 0.0011 - val_acc: 0.7967
Epoch 384/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.5632e-04 - acc: 0.8050Epoch 00383: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4000e-04 - acc: 0.8078 - val_loss: 0.0012 - val_acc: 0.7991
Epoch 385/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.8036e-04 - acc: 0.8136Epoch 00384: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.8443e-04 - acc: 0.8143 - val_loss: 0.0012 - val_acc: 0.7664
Epoch 386/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.1339e-04 - acc: 0.8086Epoch 00385: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.1523e-04 - acc: 0.8148 - val_loss: 0.0011 - val_acc: 0.8014
Epoch 387/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.9419e-04 - acc: 0.8143Epoch 00386: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.1272e-04 - acc: 0.8172 - val_loss: 0.0014 - val_acc: 0.7897
Epoch 388/400
1600/1712 [===========================>..] - ETA: 0s - loss: 8.9491e-04 - acc: 0.8131Epoch 00387: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.9674e-04 - acc: 0.8143 - val_loss: 0.0011 - val_acc: 0.7897
Epoch 389/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.8459e-04 - acc: 0.8164Epoch 00388: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.9423e-04 - acc: 0.8183 - val_loss: 0.0011 - val_acc: 0.7967
Epoch 390/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.3198e-04 - acc: 0.8119Epoch 00389: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.3256e-04 - acc: 0.8119 - val_loss: 0.0011 - val_acc: 0.7921
Epoch 391/400
1600/1712 [===========================>..] - ETA: 0s - loss: 8.6662e-04 - acc: 0.8131Epoch 00390: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.6606e-04 - acc: 0.8137 - val_loss: 0.0012 - val_acc: 0.7687
Epoch 392/400
1400/1712 [=======================>......] - ETA: 0s - loss: 9.5057e-04 - acc: 0.8021Epoch 00391: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 9.4107e-04 - acc: 0.8072 - val_loss: 0.0011 - val_acc: 0.7710
Epoch 393/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.6218e-04 - acc: 0.8150Epoch 00392: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.4963e-04 - acc: 0.8154 - val_loss: 0.0012 - val_acc: 0.7757
Epoch 394/400
1600/1712 [===========================>..] - ETA: 0s - loss: 9.1366e-04 - acc: 0.8206Epoch 00393: val_loss improved from 0.00110 to 0.00109, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 9.0694e-04 - acc: 0.8189 - val_loss: 0.0011 - val_acc: 0.8014
Epoch 395/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.9789e-04 - acc: 0.8143Epoch 00394: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.9489e-04 - acc: 0.8148 - val_loss: 0.0011 - val_acc: 0.8061
Epoch 396/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.7448e-04 - acc: 0.8264Epoch 00395: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.8372e-04 - acc: 0.8148 - val_loss: 0.0012 - val_acc: 0.7687
Epoch 397/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.6565e-04 - acc: 0.8114Epoch 00396: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.6626e-04 - acc: 0.8090 - val_loss: 0.0012 - val_acc: 0.7780
Epoch 398/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.6886e-04 - acc: 0.8114Epoch 00397: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.7156e-04 - acc: 0.8143 - val_loss: 0.0011 - val_acc: 0.7874
Epoch 399/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.9871e-04 - acc: 0.8193Epoch 00398: val_loss improved from 0.00109 to 0.00108, saving model to my_model.h5
1712/1712 [==============================] - 0s - loss: 8.9746e-04 - acc: 0.8154 - val_loss: 0.0011 - val_acc: 0.7921
Epoch 400/400
1400/1712 [=======================>......] - ETA: 0s - loss: 8.7623e-04 - acc: 0.8129Epoch 00399: val_loss did not improve
1712/1712 [==============================] - 0s - loss: 8.7179e-04 - acc: 0.8189 - val_loss: 0.0012 - val_acc: 0.7944

Step 7: Visualize the Loss and Test Predictions

(IMPLEMENTATION) Answer a few questions and visualize the loss

Question 1: Outline the steps you took to get to your final neural network architecture and your reasoning at each step.

Answer:

  1. I started with an network that I used with good results before: 3 groups of convolutional layers of doubling filter size with each convolution followed by a max pooling operation. Then followed by a global average pooling operation, a dense layer and an output dense layer that meets the 30 parameter output. All activations relu with exception of the last which is linear (I also tried tanh). Also had dropout operations set for all nodes except for output.
  2. Replaced global average pooling operation with a flatten operation since it seemed like some information was being lost. This grew the number of parameters of the model.
  3. Ran a loop choosing different optimizers to select the best using accuracy and loss (training vs validation) graphs. Selected RMSprop although adam worked well too. The 'adam' optimizer could have used a faster learning rate as the validation accuracy tracked the training accuracy well but it had lower achieved accuracy for the same number of epochs.
  4. Removed and added Convolutional layers to see if I could get better results. Adding one made the results better and had the effect of lowering the amount of parameters needed.

Question 2: Defend your choice of optimizer. Which optimizers did you test, and how did you determine which worked best?

Answer: I tested all the optimizers and chose RMSprop since it achieved the highest accuracy in 400 epochs with the network described above. The 'adam' optimizer also did well but did not reach the same accuracy in that amount of epochs. If I were to spend more time, I'd try changing the learning rate and see if I could get better results.

Use the code cell below to plot the training and validation loss of your neural network. You may find this resource useful.

In [19]:
## TODO: Visualize the training and validation loss of your neural network
print(hist.history.keys())
# summarize history for accuracy
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.yscale('log')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
dict_keys(['val_loss', 'loss', 'acc', 'val_acc'])

Question 3: Do you notice any evidence of overfitting or underfitting in the above plot? If so, what steps have you taken to improve your model? Note that slight overfitting or underfitting will not hurt your chances of a successful submission, as long as you have attempted some solutions towards improving your model (such as regularization, dropout, increased/decreased number of layers, etc).

Answer: The test loss flattens out after 200 epochs suggesting that it is overfitting. I increased the dropout on the first dense layer and decreased the number of nodes in the dense layer from 500 to 300.

Visualize a Subset of the Test Predictions

Execute the code cell below to visualize your model's predicted keypoints on a subset of the testing images.

In [20]:
y_test = model.predict(X_test)
fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X_test[i], y_test[i], ax)

Step 8: Complete the pipeline

With the work you did in Sections 1 and 2 of this notebook, along with your freshly trained facial keypoint detector, you can now complete the full pipeline. That is given a color image containing a person or persons you can now

  • Detect the faces in this image automatically using OpenCV
  • Predict the facial keypoints in each face detected in the image
  • Paint predicted keypoints on each face detected

In this Subsection you will do just this!

(IMPLEMENTATION) Facial Keypoints Detector

Use the OpenCV face detection functionality you built in previous Sections to expand the functionality of your keypoints detector to color images with arbitrary size. Your function should perform the following steps

  1. Accept a color image.
  2. Convert the image to grayscale.
  3. Detect and crop the face contained in the image.
  4. Locate the facial keypoints in the cropped image.
  5. Overlay the facial keypoints in the original (color, uncropped) image.

Note: step 4 can be the trickiest because remember your convolutional network is only trained to detect facial keypoints in $96 \times 96$ grayscale images where each pixel was normalized to lie in the interval $[0,1]$, and remember that each facial keypoint was normalized during training to the interval $[-1,1]$. This means - practically speaking - to paint detected keypoints onto a test face you need to perform this same pre-processing to your candidate face - that is after detecting it you should resize it to $96 \times 96$ and normalize its values before feeding it into your facial keypoint detector. To be shown correctly on the original image the output keypoints from your detector then need to be shifted and re-normalized from the interval $[-1,1]$ to the width and height of your detected face.

When complete you should be able to produce example images like the one below

In [21]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')


# Convert the image to RGB colorspace
image_copy = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# plot our image
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('image copy')
ax1.imshow(image_copy)
Out[21]:
<matplotlib.image.AxesImage at 0x7ff2e4690ba8>
In [22]:
### TODO: Use the face detection code we saw in Section 1 with your trained conv-net 
## TODO : Paint the predicted keypoints on the test image

# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 1.4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Display the image
fig = plt.figure(figsize = (9,9))
ax = fig.add_subplot(111)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title('Blurred Image')
ax.imshow(image_copy)

# Make a copy of the orginal image to draw face detections on
#image_with_detections = np.copy(image)

# Get the bounding box for each detected face and blur area
for (x,y,w,h) in faces:
    #image_face = image_with_faceblur[y:y+h, x:x+w]
    # Draw rectangle code copied from util.py
    rectangle = cv2.rectangle(image_copy,(x,y),(x+w,y+h),(255,0,0),3)
    ax.imshow(rectangle)
    
    # Resize face crop
    bgr_crop = image[y:y+h, x:x+w]
    orig_shape_crop = bgr_crop.shape
    gray_crop = cv2.cvtColor(bgr_crop, cv2.COLOR_BGR2GRAY)
    resize_gray_crop = cv2.resize(gray_crop, (96, 96)) / 255.
    # Run facial keypoint detector
    model = load_model("my_model.h5")
    # Plot landmarks/keypoints
    landmarks = np.squeeze(model.predict(
        np.expand_dims(np.expand_dims(resize_gray_crop, axis=-1), axis=0)))
    ax.scatter(((landmarks[0::2] * 48 + 48)*orig_shape_crop[0]/96)+x,
                ((landmarks[1::2] * 48 + 48)*orig_shape_crop[1]/96)+y,
                 marker='o', c='c', s=10)
Number of faces detected: 2

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add facial keypoint detection to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for keypoint detection and marking in the previous exercise and you should be good to go!

In [ ]:
import cv2
import time 
from keras.models import load_model
def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # keep video stream open
    while rval:
        # plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # exit by pressing any key
            # destroy windows
            cv2.destroyAllWindows()
            
            # hack from stack overflow for making sure window closes on osx --> https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()  
In [ ]:
# Run your keypoint face painter
laptop_camera_go()

(Optional) Further Directions - add a filter using facial keypoints

Using your freshly minted facial keypoint detector pipeline you can now do things like add fun filters to a person's face automatically. In this optional exercise you can play around with adding sunglasses automatically to each individual's face in an image as shown in a demonstration image below.

To produce this effect an image of a pair of sunglasses shown in the Python cell below.

In [ ]:
# Load in sunglasses image - note the usage of the special option
# cv2.IMREAD_UNCHANGED, this option is used because the sunglasses 
# image has a 4th channel that allows us to control how transparent each pixel in the image is
sunglasses = cv2.imread("images/sunglasses_4.png", cv2.IMREAD_UNCHANGED)

# Plot the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.imshow(sunglasses)
ax1.axis('off');

This image is placed over each individual's face using the detected eye points to determine the location of the sunglasses, and eyebrow points to determine the size that the sunglasses should be for each person (one could also use the nose point to determine this).

Notice that this image actually has 4 channels, not just 3.

In [ ]:
# Print out the shape of the sunglasses image
print ('The sunglasses image has shape: ' + str(np.shape(sunglasses)))

It has the usual red, blue, and green channels any color image has, with the 4th channel representing the transparency level of each pixel in the image. Here's how the transparency channel works: the lower the value, the more transparent the pixel will become. The lower bound (completely transparent) is zero here, so any pixels set to 0 will not be seen.

This is how we can place this image of sunglasses on someone's face and still see the area around of their face where the sunglasses lie - because these pixels in the sunglasses image have been made completely transparent.

Lets check out the alpha channel of our sunglasses image in the next Python cell. Note because many of the pixels near the boundary are transparent we'll need to explicitly print out non-zero values if we want to see them.

In [ ]:
# Print out the sunglasses transparency (alpha) channel
alpha_channel = sunglasses[:,:,3]
print ('the alpha channel here looks like')
print (alpha_channel)

# Just to double check that there are indeed non-zero values
# Let's find and print out every value greater than zero
values = np.where(alpha_channel != 0)
print ('\n the non-zero values of the alpha channel look like')
print (values)

This means that when we place this sunglasses image on top of another image, we can use the transparency channel as a filter to tell us which pixels to overlay on a new image (only the non-transparent ones with values greater than zero).

One last thing: it's helpful to understand which keypoint belongs to the eyes, mouth, etc. So, in the image below, we also display the index of each facial keypoint directly on the image so that you can tell which keypoints are for the eyes, eyebrows, etc.

With this information, you're well on your way to completing this filtering task! See if you can place the sunglasses automatically on the individuals in the image loaded in / shown in the next Python cell.

In [ ]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# Plot the image
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
In [ ]:
## (Optional) TODO: Use the face detection code we saw in Section 1 with your trained conv-net to put
## sunglasses on the individuals in our test image

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add the sunglasses filter to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for adding sunglasses to someone's face in the previous optional exercise and you should be good to go!

In [ ]:
import cv2
import time 
from keras.models import load_model
import numpy as np

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        # Plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [ ]:
# Load facial landmark detector model
model = load_model('my_model.h5')

# Run sunglasses painter
laptop_camera_go()